Method and system for heart rate estimation of a user

ABSTRACT

While performing heart rate estimation of a user, if the user is in motion, measured signal is prone to have noise data, which in turn affects accuracy of estimated heart rate value. Disclosed herein is a method and system for heart rate estimation when the user is in motion. The system estimates value of a noise signal present in a measured PPG signal by performing a Principle Component Analysis (PCA) of an accelerometer signal collected along with the PPG signal. The system further estimates value of a true cardiac signal for a time window, based on value of the true cardiac signal in a pre-defined number of previous time windows. The system then estimates frequency spectrum of a clean PPG signal based on the estimated noise signal and the true cardiac signal. The system further performs heart rate estimation based on the clean PPG signal.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201921035986, filed on Sep. 6, 2019. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The disclosure herein generally relates to heart rate estimation, and,more particularly, to a method and system for heart rate estimation whenthe user is in motion.

BACKGROUND

Heart rate estimation is process of estimating heart rate of a userusing appropriate sensor(s). The estimated heart rate may be used fordetermining a health condition of the user. For example, withadvancement in the field of wearable technology, many of the wearablegadgets available in the market have heart rate estimation capability asone of the features. Many of such gadgets can be configured to triggeran alert to one or more users when an abnormal variation in the heartrate of a user is detected, thereby enabling the user to seek medicalhelp when needed.

The inventors here have recognized several technical problems with suchconventional systems, as explained below. Many types of sensors andsystems are currently available for performing the heart rateestimation. Photoplethysmogram (PPG) is an example of sensors that canbe used for the heart rate estimation. The PPG can monitor the heartrate of a user by measuring variation in blood volume in skin of theuser, which is caused by pressure pulse of cardiac signal. However, whenthe user being monitored is in motion, there is high probability thatthe measured signals contain noise signals, which in turn affectsaccuracy of the heart rate estimation being performed. The state of artsystems use different approaches for estimating the heart rate of users.However, depending on the approach used, capability to handle the noisecaused by the user motion varies, which in turn affects accuracy ofresult of the health rate estimation.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a processor implemented method for heart rate estimation isprovided. In this method, a Photoplethysmogram (PPG) signal is collectedover a plurality of fixed time windows from a user being monitored, viaone or more hardware processors. Further, it is determined whether theuser was in motion while the PPG signal in each time window was beingcollected, by performing a mobility detection, via the one or morehardware processors, and further, each of the plurality of time windowsis classified as belonging to one of a set of time windows in which useris determined as in motion and a set of time windows in which user isdetermined as not in motion. Further, the heart rate estimation isperformed for the PPG signal collected over each time window in whichthe user is determined to be in motion, via the one or more hardwareprocessors. During the heart rate estimation, a noise signal isestimated by performing a Principle Component Analysis (PCA) of anaccelerometer signal collected over each of the time windows in whichthe user is determined as being in motion. Further, value of a truecardiac signal is estimated for each of the time windows in which theuser is determined as being in motion, as a trimmed mean of an obtainedpre-defined number of spectra prior to a time window being considered.Further, a spectrum of a clean PPG signal is obtained based on theestimated noise signal and the estimated value of the true cardiacsignal, using a wiener filter, and then the heart rate of the user isestimated based on the estimated spectrum of the clean PPG signal.

In another aspect, a system for heart rate estimation is provided. Thesystem includes one or more hardware processors, one or morecommunication interfaces, and one or more memory storing a plurality ofinstructions. The plurality of instructions when executed cause the oneor more hardware processors to collect a Photoplethysmogram (PPG) signalover a plurality of fixed time windows from a user being monitored, viaone or more hardware processors. Further, the system determines whetherthe user was in motion while collecting the PPG signal, by performing amobility detection, and further, each of the plurality of time windowsis classified as belonging to one of a set of time windows in which useris determined as in motion and a set of time windows in which user isdetermined as not in motion. The system then performs the heart rateestimation for the PPG signal collected over each time window in whichthe user is determined to be in motion. In this step, the systemestimates a noise signal by performing a Principle Component Analysis(PCA) of an accelerometer signal collected over each of the time windowsin which the user is determined as being in motion. The system thenestimates value of a true cardiac signal for each of the time windows inwhich the user is determined as being in motion as equal to trimmed meanof obtained pre-defined number of spectra prior to a time window beingconsidered, from a Clean Signal Buffer (CBF). The system then estimatesspectrum of a clean PPG signal based on the estimated noise signal andthe estimated value of the true cardiac signal, using a wiener filter.Further, based on the estimated spectrum of the clean PPG signal, heartrate estimation of the user is performed.

In yet another aspect, a non-transitory computer readable medium forheart rate estimation of a user is provided. The non-transitory computerreadable medium executes the following method for the heart rateestimation of the user. In this method, a Photoplethysmogram (PPG)signal is collected over a plurality of fixed time windows from a userbeing monitored, via one or more hardware processors. Further, it isdetermined whether the user was in motion while the PPG signal in eachtime window was being collected, by performing a mobility detection, viathe one or more hardware processors, and further, each of the pluralityof time windows is classified as belonging to one of a set of timewindows in which user is determined as in motion and a set of timewindows in which user is determined as not in motion. Further, the heartrate estimation is performed for the PPG signal collected over each timewindow in which the user is determined to be in motion, via the one ormore hardware processors. During the heart rate estimation, a noisesignal is estimated by performing a Principle Component Analysis (PCA)of an accelerometer signal collected over each of the time windows inwhich the user is determined as being in motion. Further, value of atrue cardiac signal is estimated for each of the time windows in whichthe user is determined as being in motion, as a trimmed mean of anobtained pre-defined number of spectra prior to a time window beingconsidered. Further, a spectrum of a clean PPG signal is obtained basedon the estimated noise signal and the estimated value of the truecardiac signal, using a wiener filter, and then the heart rate of theuser is estimated based on the estimated spectrum of the clean PPGsignal.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary system for heart rate estimation of auser, according to some embodiments of the present disclosure.

FIG. 2 is a flow diagram depicting steps involved in the process ofperforming the heart rate estimation, using the system of FIG. 1,according to some embodiments of the present disclosure.

FIG. 3 is a flow diagram depicting steps involved in the process ofperforming estimating a true cardiac signal for the purpose ofperforming the heart rate estimation using the system of FIG. 1,according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

Referring now to the drawings, and more particularly to FIG. 1 throughFIG. 3, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 illustrates an exemplary system for heart rate estimation of auser, according to some embodiments of the present disclosure. Thesystem 100 includes one or more hardware processors 102, communicationinterface(s) or input/output (I/O) interface(s) 103, and one or moredata storage devices or memory 101 operatively coupled to the one ormore hardware processors 102. The one or more hardware processors 102can be implemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,state machines, graphics controllers, logic circuitries, and/or anydevices that manipulate signals based on operational instructions. Amongother capabilities, the processor(s) are configured to fetch and executecomputer-readable instructions stored in the memory. In an embodiment,the system 100 can be implemented in a variety of computing systems,such as laptop computers, notebooks, hand-held devices, workstations,mainframe computers, servers, a network cloud and the like.

The communication interface(s) 103 can include a variety of software andhardware interfaces, for example, a web interface, a graphical userinterface, and the like and can facilitate multiple communicationswithin a wide variety of networks N/W and protocol types, includingwired networks, for example, LAN, cable, etc., and wireless networks,such as WLAN, cellular, or satellite. In an embodiment, thecommunication interface(s) 103 can include one or more ports forconnecting a number of devices to one another or to another server.

The memory 101 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, one or more components (not shown) ofthe system 100 can be stored in the memory 101. The memory 101 isconfigured to store operational instructions which when executed causeone or more of the hardware processor(s) 102 to perform various actionsassociated with the heart rate estimation being handled by the system100. The various steps involved in the process of heart rate estimationare explained with description of FIG. 2 and FIG. 3. All the steps inFIG. 2 and FIG. 3 are explained with reference to the system of FIG. 1.

FIG. 2 is a flow diagram depicting steps involved in the process ofperforming the heart rate estimation, using the system of FIG. 1,according to some embodiments of the present disclosure. So as toperform heart rate estimation of a user, the one or more hardwareprocessors 102 of the system 100 collects (202) PPG signals andaccelerometer signals from the user, wherein the collected PPG signalsand accelerometer signals are split over a plurality of time windows. Inan embodiment, length of all the time windows is same. In various otherembodiments, the length of the time windows is pre-configured ordynamically configured with the system 100, by any authorized user,using appropriate interface that may be provided by the communicationinterface(s) 103. The one or more hardware processors 102 of the system100 may perform appropriate pre-processing to clean up and condition thecollected PPG and accelerometer signals for further processing for theheart rate estimation, and may perform filtering and normalization.

For each time window in which the PPG signal has been collected, the oneor more hardware processors 102 of the system 100 performs a mobilitydetection to determine (204) whether or not the user was in motion whilethe PPG signal was collected. In an embodiment, any suitable mobilitydetection mechanism can be used by the system 100 to perform themobility detection. If the user is found to be not in motion whilecollecting the PPG signals, then heart rate estimation is performed(208) from the collected PPG signals using any suitable approach.

If the system 100 determines that the user was in motion whilecollecting the PPG signals in at least one of the plurality of timewindows, then the following method is executed for the heart rateestimation. The method is explained for data (i.e. PPG signal andaccelerometer signals) collected over one time window. It is to be notedthat the same approach is used for heart rate estimation in all timewindows in which user was determined to be in motion while collectingthe PPG signals.

The one or more hardware processors 102 of the system 100 collects andprocesses the accelerometer signal in the time window being considered,to estimate (210) a noise signal that is present in the PPG signalcollected in the same time window.

In an embodiment, the one or more hardware processors 102 of the system100 performs a Principle Component Analysis (PCA) of the accelerometersignal to estimate the noise signal. The motion of the user can beapproximated in a particular direction if a brief window of time isconsidered during which motion of the user is majorly unidirectional inan arbitrary direction. The PCA is applied on the accelerometer signalso as to find out which direction has a maximum variation inacceleration (due to the motion) the Principal Component Analysis (PCA)is applied to the acceleration signal. The PCA projects the originalsignal into orthonormal basis along which the variance is maximized.Assuming Y∈

^(T*3) is a projected matrix it is denoted as Y=AW where A∈

^(T*3) is an acceleration matrix and columns of the projection matrix W∈

^(3*3) represents eigenvector basis. As a first principle component of Ymatrix highest variance among the three orthogonal direction, it isconsidered as the direction of motion or the noise. Noise spectrum isestimated as:

P _(N)(f)≈PC1(f)∈

^(1*M)  (1)

As the highest variance of accelerometer signal is itself a marker ofmotion, this approach improves the estimate of noise.

The one or more hardware processors 102 of the system 100 furtherestimates (212) value of a true cardiac signal for the time window. Inthe time windows in which motion has been detected, the true cardiacsignal is not available, hence the estimation is required. Stepsinvolved in estimation of the true cardiac signal are depicted in FIG.3. The system 100 maintains in the memory 101, a Clean Signal Buffer(CB). The system 100 collects information pertaining to a pre-definednumber (N) of spectra prior to the time window being considered forestimation of the true cardiac signal, and stores this information inthe CB. In an embodiment, at any instance the CB comprises datapertaining to PPG signal containing noise data for a time window beingconsidered. In various embodiments, value of N is pre-configured ordynamically configured with the system 100 by an authorized user, and isstored in the memory 101. For estimation of the true cardiac signal forthe time window, the system 100 obtains (302) the spectra present in theCB and estimates (304) value of the true cardiac signal as a trimmedmean of N number of spectra, which is represented as:

$\begin{matrix}{{b_{k} = {\frac{1}{N}{\sum_{i = 1}^{N}{C{B\left( {i,k} \right)}\mspace{14mu} {\forall{k \in \left\lbrack {1,M} \right\rbrack}}}}}}{{{P_{C}(f)} \approx B} = {\left\lbrack {b_{1},\ldots \;,b_{M}} \right\rbrack \in {\mathbb{R}}^{1*M}}}} & (2)\end{matrix}$

Where M represents total number of frequency bins. Rows of the CBcontain envelops of previous N spectra. The estimation of the truecardiac signal is performed along the columns of the CB for everyfrequency bin. This leads to a row vector B∈

^(1*M) which approximates the true cardiac spectrum. This averagingprocess smoothens the signal, imparts the uniformity and curtails thehigh-frequency noises. Since intense movement of the user could causespurious noises, by taking the trimmed mean, outliers are eliminated.Later when the Weiner Filter estimates the clean PPG spectrum for thatparticular window, the noisy PPG spectrum is replaced by the clean one.

The system 100 uses the following equation to obtain coefficients forthe wiener filter.

$\begin{matrix}{{W_{opt}(f)} = \frac{P_{C}(f)}{{P_{C}(f)} + {P_{N}(f)}}} & (3)\end{matrix}$

After approximating the true cardiac spectrum and the noise spectrum,the system 100 applies (1) and (2) in (3) to obtain a final equation forwiener filter coefficients as:

$\begin{matrix}{{W_{opt}(f)} = \frac{B}{B + {{PC}_{1}(f)}}} & (4)\end{matrix}$

It is to be noted that in equation (4), all the elements are having samedimension (∈

^(1*M)) and sample-wise divisions is achieved.

Based on the estimated coefficients, the wiener filter of the system 100estimates (214) a spectrum of clean PPG for the time window, based onthe estimated noise signal and the true cardiac signals. The estimatedspectrum of clean PPG is further used by the system 100 to estimate(216) heart rate of the user at the time window being considered.

In various embodiments, steps in method 200 may be performed in the sameorder as depicted in FIG. 2 or in any alternate order that istechnically feasible. In another embodiment, one or more of the steps inmethod 200 may be omitted as per requirements.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

The embodiments of present disclosure herein address unresolved problemof heart rate estimation of a user when the user is in motion. Theembodiment thus provides a wiener filter based mechanism to estimateheart rate of the user while in motion. Moreover, the embodiments hereinfurther provide a mechanism to estimate a true cardiac signal for eachtime window in which user motion was detected, for the purpose ofestimating a spectrum of clean PPG signal which in turn is used forheart rate estimation.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A processor implemented method for heart rateestimation, comprising: collecting a Photoplethysmogram (PPG) signalover a plurality of fixed time windows from a user being monitored, viaone or more hardware processors; determining whether the user was inmotion in any of the plurality of time windows while the PPG signal wasbeing collected, by performing a mobility detection, via the one or morehardware processors; classifying each of the plurality of time windowsas belonging to one of a set of time windows in which user is determinedas in motion and a set of time windows in which user is determined asnot in motion; and performing the heart rate estimation for the PPGsignal collected over each time window belonging to the set of timewindows in which user is determined as in motion, via the one or morehardware processors, comprising: estimating a noise signal by performinga Principle Component Analysis (PCA) of an accelerometer signalcollected over each of the time windows in which the user is determinedas being in motion; estimating value of a true cardiac signal for eachof the time windows in which the user is determined as being in motion,comprising: obtaining a pre-defined number of spectra prior to a timewindow being considered, from a Clean Signal Buffer (CBF); andestimating the value of the true cardiac signal by taking a trimmed meanof the obtained pre-defined number of spectra; estimating spectrum of aclean PPG signal based on the estimated noise signal and the estimatedvalue of the true cardiac signal, using a wiener filter; and estimatingheart rate of the user based on the estimated spectrum of the clean PPGsignal.
 2. The method of claim 1, wherein the CBF comprises the PPGsignal collected from the user along with the obtained a pre-definednumber of spectra.
 3. The method of claim 1, wherein in each time windowin which the user is determined as not in motion, the heart rate isestimated from the collected PPG signal in the corresponding timewindow.
 4. A system for heart rate estimation, comprising: one or morehardware processors; one or more communication interfaces; and one ormore memory storing a plurality of instructions, wherein the pluralityof instructions when executed cause the one or more hardware processorsto: collect a Photoplethysmogram (PPG) signal over a plurality of fixedtime windows from a user being monitored; determine whether the user wasin motion in any of the plurality of time windows while the PPG signalwas being collected, by performing a mobility detection; classify eachof the plurality of time windows as belonging to one of a set of timewindows in which user is determined as in motion and a set of timewindows in which user is determined as not in motion; and perform theheart rate estimation for the PPG signal collected over each time windowbelonging to the set of time windows in which user is determined as inmotion, by: estimating a noise signal by performing a PrincipleComponent Analysis (PCA) of an accelerometer signal collected over eachof the time windows in which the user is determined as being in motion;estimating value of a true cardiac signal for each of the time windowsin which the user is determined as being in motion, comprising:obtaining a pre-defined number of spectra prior to a time window beingconsidered, from a Clean Signal Buffer (CBF); and estimating the valueof the true cardiac signal by taking a trimmed mean of the obtainedpre-defined number of spectra; estimating spectrum of a clean PPG signalbased on the estimated noise signal and the estimated value of the truecardiac signal, using a wiener filter; and estimating heart rate of theuser based on the estimated spectrum of the clean PPG signal.
 5. Thesystem of claim 4, wherein the CBF comprises the PPG signal collectedfrom the user along with the obtained pre-defined number of spectra. 6.The system of claim 4, wherein the system estimates the heart rate ofthe user in each time window in which the user is determined as not inmotion, from the collected PPG signal in the corresponding time window.7. A non-transitory computer readable medium for heart rate estimation,wherein the non-transitory computer readable medium when executed by oneor more hardware processors, cause the heart rate estimation by:collecting a Photoplethysmogram (PPG) signal over a plurality of fixedtime windows from a user being monitored, via one or more hardwareprocessors; determining whether the user was in motion in any of theplurality of time windows while the PPG signal was being collected, byperforming a mobility detection, via the one or more hardwareprocessors; classifying each of the plurality of time windows asbelonging to one of a set of time windows in which user is determined asin motion and a set of time windows in which user is determined as notin motion; and performing the heart rate estimation for the PPG signalcollected over each time window belonging to the set of time windows inwhich user is determined as in motion, via the one or more hardwareprocessors, comprising: estimating a noise signal by performing aPrinciple Component Analysis (PCA) of an accelerometer signal collectedover each of the time windows in which the user is determined as beingin motion; estimating value of a true cardiac signal for each of thetime windows in which the user is determined as being in motion,comprising: obtaining a pre-defined number of spectra prior to a timewindow being considered, from a Clean Signal Buffer (CBF); andestimating the value of the true cardiac signal by taking a trimmed meanof the obtained pre-defined number of spectra; estimating spectrum of aclean PPG signal based on the estimated noise signal and the estimatedvalue of the true cardiac signal, using a wiener filter; and estimatingheart rate of the user based on the estimated spectrum of the clean PPGsignal.
 8. The non-transitory computer readable medium of claim 7,wherein the CBF comprises the PPG signal collected from the user alongwith the obtained pre-defined number of spectra.
 9. The non-transitorycomputer readable medium of claim 7, wherein in each time window inwhich the user is determined as not in motion, the heart rate isestimated from the collected PPG signal in the corresponding timewindow.